Apparatus and method for estimating blood sugar based on heterogeneous spectrums

ABSTRACT

An apparatus is provided. The apparatus includes a memory, a second type spectrum measurer, and a processor. The memory stores an individualized blood sugar estimation model. The second type spectrum measurer measures second type spectrum data for a skin of a user. The processor calculates blood sugar of the user based on the measured second type spectrum data and the individualized blood sugar model. The blood sugar model is generated based on blood sugar profile data of the user calculated based on a first type spectrum-blood sugar profile relationship model and training second type spectrum data for the skin of the user.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Divisional application of U.S. application Ser.No. 15/417,408, filed Jan. 27, 2017, which claims priority from KoreanPatent Application No. 10-2016-0091622, filed on Jul. 19, 2016, in theKorean Intellectual Property Office, the disclosures of each of whichare incorporated by reference herein in their entireties.

BACKGROUND 1. Field

An apparatus and a method consistent with exemplary embodiments relateto a blood sugar estimation technology, and more particularly, to anapparatus and a method of estimating blood sugar using heterogeneousspectrums.

2. Description of Related Art

Nowadays, due to the living environment, adult diseases increased,causing users to be more interested in health than before. Among them,patients who suffer from adult diseases including high blood pressure,diabetes, etc. are increasing. In case of such chronic disease, whilethe clinic visits are necessary, it is also necessary for the patientsto perform follow-up examinations of their conditions by periodicallychecking blood pressure and blood sugar level and take appropriateactions accordingly. For example, it is necessary for people withdiabetes to monitor blood sugar about six times a day to control andmaintain an appropriate blood sugar level by periodically measuring thelevel of sugar in blood.

Accordingly, the speed at which the use of the portable personal medicaldevices including blood pressure gauges, blood sugar meters, insulinpumps, etc. spreads is rapidly increasing. According to this trend,standardization of medical devices and medical services such asdescribed above is becoming active, and personal medical devices andservices utilizing them are being provided.

Currently, as one of the medical devices that measure blood sugar, thereare the invasive blood sugar meters. In the method of using such aninvasive blood sugar meter described above, a needle penetrates the skinand blood is directly collected to check the level of sugar in blood.However, in this method, a patient may feel pain from being pricked witha needle every time during a blood-collecting process, and there is arisk of infection of a part that is pricked with the needle. Sinceinvasive blood sugar meters may make users uncomfortable, as describedabove, noninvasive blood sugar meters based on a spectroscopic analysismethod capable of measuring a blood sugar level of intercellular liquidunder the skin without using a needle have been developed.

However, even when using noninvasive blood sugar meters, a plurality ofblood-collecting processes are still necessary to generate calibration,that is, an individualized blood sugar estimation model.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription of Exemplary Embodiments. This summary is not intended toidentify key features or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in determining the scopeof the claimed subject matter.

The following description relates to an apparatus and a method fordetermining blood sugar level using heterogeneous spectrums.

In one general aspect of an exemplary embodiment, a blood sugar modelgeneration apparatus includes a data obtainer configured to obtain bloodsugar profile data of a user based on a first type spectrum-blood sugarprofile relationship model, a second type spectrum measurer configuredto measure training second type spectrum data for the skin of the user,and a processor configured to generate an individualized blood sugarmodel based on the obtained blood sugar profile data and the measuredtraining second type spectrum data.

A first type spectrum may be a Raman spectrum, and a second typespectrum may be a near infrared (NIR) spectrum.

The first type spectrum-blood sugar profile relationship model may begenerated through machine learning based on training first type spectrumdata and training blood sugar profile data.

The training blood sugar profile data may be obtained by performing anoral glucose tolerance test (OGTT) on a subject whose training firsttype spectrum data has been measured.

The first type spectrum-blood sugar profile relationship model may begenerated through machine learning with the training first type spectrumdata as an input and the training blood sugar profile data as a target.

The first type spectrum-blood sugar profile relationship model may begenerated through machine learning with the training first type spectrumdata as an input and an area under the curve (AUC) value calculated fromthe training blood sugar profile data as a target.

A machine learning algorithm may be one of partial least squaresregression, linear regression, neural network, decision tree, geneticalgorithm, genetic programming, K-nearest neighbor, radial basisfunction network, random, forest, support vector machine, anddeep-learning.

The second type spectrum measurer may include a light source configuredto emit light to the skin of the user and a spectroscope configured todetect absorbed, scattered, or reflected light from the skin of the userand measures the training second type spectrum data based on thedetected light.

The processor may generate the individualized blood sugar model throughmachine learning.

The processor may generate the individualized blood sugar model bycalculating a blood sugar value corresponding to the measured trainingsecond type spectrum data from the obtained blood sugar profile data andthrough machine learning with the training second type spectrum data asan input and the calculated blood sugar value as a target.

In another general aspect of an exemplary embodiment, a blood sugarprofile providing apparatus includes a memory configured to store afirst type spectrum-blood sugar profile relationship model, a first typespectrum measurer configured to measure first type spectrum data for theskin of a user, and a processor configured to calculate a blood sugarprofile of the user based on the first type spectrum-blood sugar profilerelationship model and the measured first type spectrum data.

A first type spectrum may be a Raman spectrum.

The first type spectrum-blood sugar profile relationship model may begenerated through machine learning based on training first type spectrumdata and training blood sugar profile data.

The training blood sugar profile data may be obtained by performing anOGTT on a subject whose training first type spectrum data has beenmeasured.

The first type spectrum-blood sugar profile relationship model may begenerated through machine learning with the training first type spectrumdata as an input and the training blood sugar profile data as a target.

The first type spectrum-blood sugar profile relationship model may begenerated through machine learning with the training first type spectrumdata as an input and an AUC value calculated from the training bloodsugar profile data as a target.

A machine learning algorithm may be one of partial least squaresregression, linear regression, neural network, decision tree, geneticalgorithm, genetic programming, K-nearest neighbor, radial basisfunction network, random, forest, support vector machine, anddeep-learning.

The first type spectrum measurer may include a light source configuredto emit light to the skin of the user and a spectroscope configured todetect absorbed, scattered, or reflected light from the skin of the userand measures the first type spectrum data.

When the first type spectrum-blood sugar profile relationship model isgenerated through machine learning with training first type spectrumdata as an input and training blood sugar profile data as a target, theprocessor may calculate a blood sugar profile of the user by a bloodprofile output by inputting the measured first type spectrum data intothe first type spectrum-blood sugar profile relationship model.

When the first type spectrum-blood sugar profile relationship model isgenerated through machine learning with training first type spectrumdata as an input and an AUC valued calculated from training blood sugarprofile data as a target, the processor may calculate a blood sugarprofile of the user based on an AUC value output by inputting themeasured first type spectrum data into the first type spectrum-bloodsugar profile relationship model.

The processor may adjust a reference blood sugar profile to allow an AUCvalue of the reference blood sugar profile to be the output AUC valueand may calculate a blood sugar profile data of the user by the data ofthe adjusted reference blood sugar profile.

In still another general aspect of an exemplary embodiment, an apparatusincludes a memory configured to store an individualized blood sugarmodel, a second type spectrum measurer configured to measure second typespectrum data for the skin of a user, and a processor configured tocalculate blood sugar of the user based on the measured second typespectrum data and the individualized blood sugar model. Here, the bloodsugar model is generated based on blood sugar profile data of the userbased on a first type spectrum-blood sugar profile relationship modeland training second type spectrum data for the skin of the user.

A first type spectrum may be a Raman spectrum, and a second typespectrum may be an NIR spectrum.

The first type spectrum-blood sugar profile relationship model may begenerated through machine learning based on training first type spectrumdata and training blood sugar profile data.

The training blood sugar profile data may be obtained by performing anOGTT on a subject whose training first type spectrum data has beenmeasured.

The first type spectrum-blood sugar profile relationship model may begenerated through machine learning with the training first type spectrumdata as an input and the training blood sugar profile data as a target.

The first type spectrum-blood sugar profile relationship model may begenerated through machine learning with the training first type spectrumdata as an input and an AUC value calculated from the training bloodsugar profile data as a target.

A machine learning algorithm may be one of partial least squaresregression, linear regression, neural network, decision tree, geneticalgorithm, genetic programming, K-nearest neighbor, radial basisfunction network, random, forest, support vector machine, anddeep-learning.

The second type spectrum measurer may include a light source configuredto emit light to the skin of the user and a spectroscope configured todetect absorbed, scattered, or reflected light from the skin of the userand measures the second type spectrum data based on the detected light.

The individualized blood sugar model may be generated through machinelearning.

Other features and aspects of exemplary embodiment will become moreapparent from the following detailed description of exemplaryembodiment, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or other aspects will be more apparent and more readilyappreciated by describing from the following description of exemplaryembodiments with reference to the accompanying drawings, in which:

FIG. 1 is a block diagram illustrating a heterogeneous spectrum-basedblood sugar estimation apparatus according to an exemplary embodiment.

FIG. 2 is a block diagram illustrating a first type spectrum-blood sugarprofile relationship model generation apparatus according to anexemplary embodiment.

FIG. 3 is a block diagram illustrating another example of a first typespectrum-blood sugar profile relationship model generation apparatusaccording to an exemplary embodiment.

FIG. 4 is a block diagram illustrating a blood sugar profile estimationapparatus according to an exemplary embodiment.

FIG. 5 is a block diagram illustrating another example of a blood sugarprofile estimation apparatus according to an exemplary embodiment.

FIG. 6 is a view illustrating a method of estimating blood sugar profiledata according to an exemplary embodiment.

FIG. 7 is a block diagram illustrating a blood sugar estimation modelgeneration apparatus according to an exemplary embodiment.

FIG. 8 is a block diagram illustrating another example of a blood sugarestimation model generation apparatus according to an exemplaryembodiment.

FIG. 9 is a block diagram illustrating a blood sugar estimationapparatus according to an exemplary embodiment.

FIG. 10 is a block diagram illustrating another example of a blood sugarestimation apparatus according to an exemplary embodiment.

FIG. 11 is a flowchart illustrating a method of estimating blood sugarbased on a heterogeneous spectrum according to an exemplary embodiment.

FIG. 12 is a flowchart illustrating a method of generating a first typespectrum-blood sugar profile relationship model according to anexemplary embodiment.

FIG. 13 is a flowchart illustrating a method of estimating a blood sugarprofile according to an exemplary embodiment.

FIG. 14 is a flowchart illustrating a method of generating a blood sugarestimation model according to an exemplary embodiment.

FIG. 15 is a flowchart illustrating a method of estimating blood sugaraccording to an exemplary embodiment.

Throughout the drawings and the detailed description, unless otherwisedescribed, the same drawing reference numerals will be understood torefer to the same elements, features, and structures. The relative sizeand depiction of these elements may be exaggerated for clarity,illustration, and convenience.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The following description is provided to assist the reader in gaining acomprehensive understanding of the methods, apparatuses, and/or systemsdescribed herein. Accordingly, various changes, modifications, andequivalents of the methods, apparatuses, and/or systems described hereinwill be suggested to those of ordinary skill in the art. Also,descriptions of well-known functions and constructions may be omittedfor increased clarity and conciseness.

FIG. 1 is a block diagram illustrating a heterogeneous spectrum-basedblood sugar estimation apparatus according to an exemplary embodiment.

Referring to FIG. 1 , a heterogeneous spectrum-based blood sugarestimation apparatus 100 may include a first type spectrum-blood sugarprofile relationship model generator 110, a blood sugar profileestimator 120, a blood sugar estimation model generator 130, and a bloodsugar estimator 140.

The first type spectrum-blood sugar profile relationship model generator110 may generate a model which defines a relationship between a firsttype spectrum and a blood sugar profile (hereinafter, a first typespectrum-blood sugar profile relationship model). Here, the first typespectrum may be a Raman spectrum for the skin and the blood sugarprofile may be a trend of blood sugar in time.

According to an exemplary embodiment, the first type spectrum-bloodsugar profile relationship model generator 110 may generate the firsttype spectrum-blood sugar profile relationship model through machinelearning based on training first type spectrum data and training bloodsugar profile data for skin of a subject. Here, the training blood sugarprofile data may be data obtained by performing an oral glucosetolerance test (OGTT) on the subject whose first type spectrum data hasbeen measured. Also, a machine learning algorithm may include partialleast squares regression, linear regression, neural network, decisiontree, genetic algorithm, genetic programming, K-nearest neighbor, radialbasis function network, random, forest, support vector machine,deep-learning, etc. However, the learning algorithm is provided by wayof an example and is not limited thereto.

The blood sugar profile estimator 120 may estimate blood sugar profiledata of a user.

According to an exemplary embodiment, the blood sugar profile estimator120 may estimate blood sugar profile data using a first typespectrum-blood sugar profile relationship model. For example, the bloodsugar profile estimator 120 may obtain first type spectrum data for theskin of the user and may estimate blood sugar profile data of the userby inputting the obtained first type spectrum data into the first typespectrum-blood sugar profile relationship model.

The blood sugar estimation model generator 130 may generate anindividualized blood sugar estimation model.

According to an exemplary embodiment, the blood sugar estimation modelgenerator 130 may generate the individualized blood sugar estimationmodel through machine learning based on training second type spectrumdata for the skin of the user and the blood sugar profile data of theuser estimated by the blood sugar profile estimator 120. Here, a secondtype spectrum may be a near-infrared (NIR) spectrum.

For example, the blood sugar estimation model generator 130 may generatethe individualized blood sugar estimation model through machine learningwith the training second type spectrum data for the skin of the user asan input and a blood sugar value corresponding to the training secondtype spectrum data as a target. Here, the blood sugar valuecorresponding to the training second type spectrum data may becalculated from the estimated blood sugar profile data of the user.

The blood sugar estimator 140 may estimate blood sugar of the user.

According to an exemplary embodiment, the blood sugar estimator 140 mayestimate the blood sugar of the user using the individualized bloodsugar estimation model. For example, the blood sugar estimator 140 mayobtain the second type spectrum data for the skin of the user and mayestimate the blood sugar of the user by inputting the obtained secondtype spectrum into the blood sugar estimation model.

Meanwhile, the heterogeneous spectrum-based blood sugar estimationapparatus 100 may be embodied as a single software module ormanufactured as a single hardware chip to be installed in an electronicapparatus. Otherwise, each of the components that form the heterogeneousspectrum-based blood sugar estimation apparatus 100 such as the firsttype spectrum-blood sugar profile relationship model generator 110, theblood sugar profile estimator 120, the blood sugar estimation modelgenerator 130, and the blood sugar estimator 140 may be embodied as aseparate software module or a separate hardware chip to be installed ina separate electronic apparatus. Here, the electronic apparatus mayinclude a fixed terminal and a mobile terminal. The fixed terminal mayinclude a digital television (TV), a desktop computer, etc. The mobileterminal may include a cellular phone, a smart phone, a tablet personalcomputer (PC), a notebook PC, personal digital assistants (PDA), aportable multimedia player (PMP), a navigation device, an MP3 player, adigital camera, a wearable device, etc. However, the electronic deviceis not limited to the above examples and may include various devices.

FIG. 2 is a block diagram illustrating a first type spectrum-blood sugarprofile relationship model generation apparatus according to anexemplary embodiment. A first type spectrum-blood sugar profilerelationship model generation apparatus 200 may be an example of thefirst type spectrum-blood sugar profile relationship model generator 110of FIG. 1 .

Referring to FIG. 2 , the first type spectrum-blood sugar profilerelationship model generation apparatus 200 may include a data obtainer210 and a processor 220.

The data obtainer 210 may obtain training first type spectrum data andtraining blood sugar profile data. Here, a first type spectrum may be aRaman spectrum.

According to an exemplary embodiment, the data obtainer 210 may obtainthe training first type spectrum data and the training blood sugarprofile data from a certain database or an external apparatus. Here, thetraining blood sugar profile data may be data obtained by performing anOGTT on a subject whose first type spectrum data has been measured.

The processor 220 may generate the first type spectrum-blood sugarprofile relationship model through machine learning based on thetraining first type spectrum data and the training blood sugar profiledata which are obtained. Here, a machine learning algorithm may includepartial least squares regression, linear regression, neural network,decision tree, genetic algorithm, genetic programming, K-nearestneighbor, radial basis function network, random, forest, support vectormachine, deep-learning, etc. but is not limited thereto.

According to an exemplary embodiment, the processor 220 may generate thefirst type spectrum-blood sugar profile relationship model throughmachine learning with the obtained training first type spectrum data asan input and training blood sugar profile data as a target.

According to another exemplary embodiment, the processor 220 maygenerate the first type spectrum-blood sugar profile relationship modelby calculating an area under the curve (AUC) value of a training bloodsugar profile from the training blood sugar profile data and throughmachine learning with the training first type spectrum data as an inputand the calculated AUC value as a target.

FIG. 3 is a block diagram illustrating another example of a first typespectrum-blood sugar profile relationship model generation apparatusaccording to an exemplary embodiment.

Referring to FIG. 3 , compared with the first type spectrum-blood sugarprofile relationship model generation apparatus 200 of FIG. 2 , a firsttype spectrum-blood sugar profile relationship model generationapparatus 300 may selectively further include an inputter 310, a storageor a memory 320, a communicator 330, and an outputter 340.

The inputter or an input interface 310 may receive various operationsignals from a user. According to an exemplary embodiment, the inputter310 may include a key pad, a dome switch, a touch pad (staticpressure/static electricity), a jog wheel, a jog switch, a hardware(H/W) button, etc. Particularly, when a touch pad and a display togetherform a layer structure, it may be called a touch screen.

The storage 320 may store programs or commands for operations of thefirst type spectrum-blood sugar profile relationship model generationapparatus 300 and may store data which are input or output. Also, thestorage 320 may store a generated first type spectrum-blood sugarprofile relationship model.

The storage 320 may include a flash memory type memory, a hard disk typememory, a multimedia card micro type memory, a card type memory such asa secure digital (SD) memory, an extreme digital (XD) memory, etc., arandom access memory (RAM), a static random access memory (SRAM), a readonly memory (ROM), an electrically erasable programmable read onlymemory (EEPROM), a programmable read only memory (PROM), a magneticmemory, a magnetic disk, an optical disk, etc. Also, the first typespectrum-blood sugar profile relationship model generation apparatus 300may operate an external storage medium such as a web storage whichperforms a storage function of the storage 320 on the Internet.

The communicator 330 may perform communication with an externalapparatus. For example, the communicator 330 may transmit data inputthrough the inputter 310, the first type spectrum-blood sugar profilerelationship model generated by the processor 220, etc. to the externalapparatus or may receive data for generating the first typespectrum-blood sugar profile relationship model from the externalapparatus.

Here, the external apparatus may be a medical apparatus which uses thegenerated first type spectrum-blood sugar profile relationship model, aprinter for outputting a result, or a display apparatus which displaysdata related to the generated first type spectrum-blood sugar profilerelationship model. In addition, the external apparatus may be a digitalTV, a desktop computer, a cellular phone, a smart phone, a tablet PC, anotebook PC, PDA, a PMP, a navigation device, an MP3 player, a digitalcamera, a wearable device, etc. but is not limited thereto.

The outputter 340 may output data related to generating the first typespectrum-blood sugar profile relationship model and data related to thegenerated first type spectrum-blood sugar profile relationship model.According to an exemplary embodiment, the outputter 340 may output thedata related to generating the first type spectrum-blood sugar profilerelationship model and the data related to the generated first typespectrum-blood sugar profile relationship model using at least one of anacoustic method, a visual method, and a tactile method. For example, theoutputter 340 may output the data related to generating the first typespectrum-blood sugar profile relationship model and the data related tothe generated first type spectrum-blood sugar profile relationship modelusing a voice, a text, vibrations, etc. For this, the outputter 340 mayinclude a display, a speaker, a vibrator, etc.

FIG. 4 is a block diagram illustrating a blood sugar profile estimationapparatus according to an exemplary embodiment. A blood sugar profileestimation apparatus 400 may be an example of the blood sugar profileestimator 120 of FIG. 1 .

Referring to FIG. 4 , the blood sugar profile estimation apparatus 400may include a first type spectrum measurer 410, a storage 420, and aprocessor 430.

The first type spectrum measurer 410 may measure first type spectrumdata, for example, Raman spectrum data for the skin of the user. Forthis, the first type spectrum measurer 410 may include a light source411 which emits light to the skin of the user and a spectroscope 412which detects absorbed, scattered, or reflected light from the skin ofthe user and measures the first type spectrum data. Here, the lightsource 411 may include a light emitting diode (LED), a laser diode, etc.The spectroscope 412 may include a photo diode, a photo transistor(PTr), a charge-coupled device (CCD), etc.

The storage 420 may store a first type spectrum-blood sugar profilerelationship model. Here, the first type spectrum-blood sugar profilerelationship model may be generated through machine learning withtraining first type spectrum data as an input and training blood sugarprofile data as a target or with training first type spectrum data as aninput and an AUC value of a training blood sugar profile calculated fromthe training blood sugar profile data as a target.

Also, the storage 420 may store programs or commands for operating theblood sugar profile estimation apparatus 400 and may store data whichare input or output.

The storage 420 may include a flash memory type memory, a hard disk typememory, a multimedia card micro type memory, a card type memory such asan SD memory, an XD memory, etc., an RAM, an SRAM, an ROM, an EEPROM, aPROM, a magnetic memory, a magnetic disk, an optical disk, etc. Also,the blood sugar profile estimation apparatus 400 may operate an externalstorage medium such as a web storage which performs a storage functionof the storage 420 on the Internet.

The processor 430 may estimate blood sugar profile data of the userbased on the measured first type spectrum data and the stored first typespectrum-blood sugar profile relationship model.

According to an exemplary embodiment, when the first type spectrum-bloodsugar profile relationship model is generated through machine learningwith the training first type spectrum data as the input and the trainingblood sugar profile data as the target, an output of the first typespectrum-blood sugar profile relationship model is provided as the bloodsugar profile data. In this case, the processor 430 may estimate theblood sugar profile data output by inputting the measured first typespectrum data into the first type spectrum-blood sugar profilerelationship model as the blood sugar profile data of the user.

According to another exemplary embodiment, when the first typespectrum-blood sugar profile relationship model may be generated throughmachine learning with the training first type spectrum data as the inputand the AUC value of the training blood sugar profile calculated fromthe training blood sugar profile data as the target, an output of thefirst type spectrum-blood sugar profile relationship model is providedin the form of AUC, that is, the AUC value of the blood sugar profile ofthe user to be estimated. In this case, the processor 430 may estimatethe blood sugar profile data of the user based on the AUC value outputby inputting the measured first type spectrum data into the first typespectrum-blood sugar profile relationship model. For example, theprocessor 430 may adjust a reference blood sugar profile to allow an AUCvalue of the reference blood sugar profile to be the output AUC value,for example, adjustment of the height of a graph, and may estimate theadjusted reference blood sugar profile as the blood sugar profile of theuser. Here, the reference blood sugar profile may be experimentallyderived as a blood sugar profile capable of expressing blood sugarprofiles of a plurality of subjects.

FIG. 5 is a block diagram illustrating another example of a blood sugarprofile estimation apparatus according to an exemplary embodiment.

Referring to FIG. 5 , compared with the blood sugar profile estimationapparatus 400 of FIG. 4 , a blood sugar profile estimation apparatus 500may further include an inputter 510, a communicator 520, and anoutputter 530 selectively.

The inputter or an input interface 510 may receive various operationsignals from a user. According to an exemplary embodiment, the inputter510 may include a key pad, a dome switch, a touch pad (staticpressure/static electricity), a jog wheel, a jog switch, an hardwarebutton, etc. Particularly, when a touch pad and a display together forma layer structure, it may be called a touch screen.

The communicator 520 may perform communication with an externalapparatus. For example, the communicator 520 may transmit data inputthrough the inputter 510, blood sugar profile data of the user estimatedby the processor 430, etc. to the external apparatus or may receive datafor estimating the blood sugar profile data of the user from theexternal apparatus.

Here, the external apparatus may be a medical apparatus which uses theestimated blood sugar profile data, a printer for outputting results, ora display apparatus which displays the estimated blood sugar profiledata. In addition, the external apparatus may be a digital TV, a desktopcomputer, a cellular phone, a smart phone, a tablet PC, a notebook PC,PDA, a PMP, a navigation device, an MP3 player, a digital camera, awearable device, etc. but is not limited thereto.

The outputter 530 may output data related to estimation of the bloodsugar profile data of the user and estimation result data. According toan exemplary embodiment, the outputter 530 may output the data relatedto estimation of the blood sugar profile of the user and the estimationresult data using at least one of an acoustic method, a visual method,and a tactile method. For example, the outputter 530 may output the datarelated to estimation of the blood sugar profile data of the user andthe estimation result data using a voice, a text, vibrations, etc. Forthis, the outputter 530 may include a display, a speaker, a vibrator,etc.

FIG. 6 is a view illustrating a method of estimating blood sugar profiledata according to an exemplary embodiment.

As described above, when a first type spectrum-blood sugar profilerelationship model may be generated through machine learning withtraining first type spectrum data as an input and an AUC value of atraining blood sugar profile calculated from training blood sugarprofile data as a target, an output of the first type spectrum-bloodsugar profile relationship model is provided in the form of AUC; thatis, an AUC value of a blood sugar profile of a user to be estimated. Inthis case, the blood sugar profile estimation apparatus 400 may adjust areference blood sugar profile to allow an AUC value of the referenceblood sugar profile to be an AUC value which is output and may estimatedata of the adjusted reference blood sugar profile as blood sugarprofile data of the user. Here, the adjustment of the reference bloodsugar profile may be performed by adjusting the height of a graph whilemaintaining a graph shape of the reference blood sugar profile.

In the example of FIG. 6 , as a result of inputting measured first typespectrum data of the user into the first type spectrum-blood sugarprofile relationship model, Case 1 indicates that the output of thefirst type spectrum-blood sugar profile relationship model is AUC=90,and Case 2 indicates that the output of the first type spectrum-bloodsugar profile relationship model is AUC=110.

In Case 1, since the output AUC value of the first type spectrum-bloodsugar profile relationship model is 90, the blood sugar profileestimation apparatus 400 adjusts a reference blood sugar profile 610 toallow an AUC value of the reference blood sugar profile 610 to be 90 andestimates data of an adjusted reference blood sugar profile 620 as theblood sugar profile data of the user.

In Case 2, since the output AUC value of the first type spectrum-bloodsugar profile relationship model is 110, the blood sugar profileestimation apparatus 400 adjusts the reference blood sugar profile 610to allow the AUC value of the reference blood sugar profile 610 to be110 and estimates data of the adjusted reference blood sugar profile asthe blood sugar profile data of the user.

FIG. 7 is a block diagram illustrating a blood sugar estimation modelgeneration apparatus according to an exemplary embodiment. A blood sugarestimation model generation apparatus 700 may be an example of the bloodsugar estimation model generator 130 of FIG. 1 .

Referring to FIG. 7 , the blood sugar estimation model generationapparatus 700 may include a data obtainer 710, a second type spectrummeasurer 720, and a processor 730.

The data obtainer 710 may obtain blood sugar profile data of a user.Here, the blood sugar profile data of the user is estimated based on afirst type spectrum-blood sugar profile relationship model and firsttype spectrum data for the skin of the user and may be obtained from acertain database or an external apparatus. Here, a first type spectrummay be a Raman spectrum.

Meanwhile, the first type spectrum-blood sugar profile relationshipmodel may be generated through machine learning with training first typespectrum data as an input and training blood sugar profile data as atarget or with training first type spectrum data as an input and an AUCvalue calculated from the training blood sugar profile data as a target.The training blood sugar profile data may be obtained by performing anOGTT on a subject whose training first type spectrum data has beenmeasured.

The second type spectrum measurer 720 may measure training second typespectrum data for the skin of the user. Here, a second type spectrum maybe an NIR spectrum. For this, the second type spectrum measurer 720 mayinclude a light source 721 which emits light to the skin of the user anda spectroscope 722 which detects absorbed, scattered, or reflected lightfrom the skin of the user and measures second type spectrum data. Here,the light source 721 may include an LED, a laser diode, etc. Thespectroscope 722 may include a photo diode, a PTr, a CCD, etc.

The processor 730 may generate an individualized blood sugar estimationmodel based on the obtained blood sugar profile data of the user and themeasured training second type spectrum data.

According to an exemplary embodiment, the processor 730 may generate theindividualized blood sugar estimation model by calculating a blood sugarvalue corresponding to the training second type spectrum data from theblood sugar profile data of the user and through machine learning withthe training second type spectrum as an input and the calculated bloodsugar value as a target. For example, as described above, since theblood sugar profile data indicates a trend of blood sugar according totime, the processor 730 may calculate a blood sugar value correspondingto a time of measuring the training second type spectrum data from theblood sugar profile data of the user.

Meanwhile, as described above, a machine learning algorithm may includepartial least squares regression, linear regression, neural network,decision tree, genetic algorithm, genetic programming, K-nearestneighbor, radial basis function network, random, forest, support vectormachine, deep-learning, etc. but is not limited thereto.

FIG. 8 is a block diagram illustrating another example of a blood sugarestimation model generation apparatus according to an exemplaryembodiment.

Referring to FIG. 8 , compared with the blood sugar estimation modelgeneration apparatus 700 of FIG. 7 , a blood sugar estimation modelgeneration apparatus 800 may further include an inputter 810, a storage820, a communicator 830, and an outputter 840 selectively.

The inputter 810 may receive various operation signals from a user.According to an exemplary embodiment, the inputter 810 may include a keypad, a dome switch, a touch pad (static pressure/static electricity), ajog wheel, a jog switch, a hardware button, etc. Particularly, when atouch pad and a display together form a layer structure, it may becalled a touch screen.

The storage 820 may store programs or commands for operating the bloodsugar estimation model generation apparatus 800 and may store data whichare input or output. Also, the storage 820 may store a generatedindividualized blood sugar estimation model.

The storage 820 may include a flash memory type memory, a hard disk typememory, a multimedia card micro type memory, a card type memory such asan SD memory, an XD memory, etc., an RAM, an SRAM, an ROM, an EEPROM, aPROM, a magnetic memory, a magnetic disk, an optical disk, etc. Also,the blood sugar estimation model generation apparatus 800 may operate anexternal storage medium such as a web storage which performs a storagefunction of the storage 820 on the Internet.

The communicator 830 may perform communication with an externalapparatus. For example, the communicator 830 may transmit data inputthrough the inputter 810, an individualized blood sugar estimation modelgenerated by the processor 730, etc. to the external apparatus or mayreceive data for generating the individualized blood sugar estimationmodel from the external apparatus.

Here, the external apparatus may be a medical apparatus which uses theindividualized blood sugar estimation mode, a printer for outputtingresults, or a display apparatus which displays the generatedindividualized blood sugar estimation model. In addition, the externalapparatus may be a digital TV, a desktop computer, a cellular phone, asmart phone, a tablet PC, a notebook PC, PDA, a PMP, a navigationdevice, an MP3 player, a digital camera, a wearable device, etc. but isnot limited thereto.

The outputter 840 may output data related to generation of theindividualized blood sugar estimation model and data related to thegenerated individualized blood sugar estimation model. According to anexemplary embodiment, the outputter 840 may output the data related togeneration of the individualized blood sugar estimation model and thedata related to the generated individualized blood sugar estimationmodel using at least one of an acoustic method, a visual method, and atactile method. For example, the outputter 840 may output the datarelated to generation of the individualized blood sugar estimation modeland data related to the generated individualized blood sugar estimationmodel using a voice, a text, vibrations, etc. For this, the outputter840 may include a display, a speaker, a vibrator, etc.

FIG. 9 is a block diagram illustrating a blood sugar estimationapparatus according to an exemplary embodiment. A blood sugar estimationapparatus 900 may be an example of the blood sugar estimator 140 of FIG.1 .

Referring to FIG. 9 , the blood sugar estimation apparatus 900 mayinclude a second type spectrum measurer 910, a storage 920, and aprocessor 930.

The second type spectrum measurer 910 may measure second type spectrumdata, for example, NIR spectrum data for the skin of the user. For this,the second type spectrum measurer 910 may include a light source 911which emits light to the skin of the user and a spectroscope 912 whichdetects absorbed, scattered, or reflected light from the skin of theuser and measures the second type spectrum data. Here, the light source911 may include an LED, a laser diode, etc. The spectroscope 912 mayinclude a photo diode, a PTr, a CCD, etc.

The storage 920 may store an individualized blood sugar estimationmodel. Here, the individualized blood sugar estimation model may begenerated based on blood sugar profile data of the user estimated by afirst type spectrum-blood sugar profile relationship model and firsttype spectrum data for the skin of the user and training second typespectrum data. For example, the individualized blood sugar estimationmodel may be generated by calculating a blood sugar value correspondingto the training second type spectrum data from the estimated blood sugarprofile data of the user and through machine learning with the trainingsecond type spectrum as an input and the calculated blood sugar value asa target.

Meanwhile, the first type spectrum-blood sugar profile relationshipmodel may be generated through machine learning with training first typespectrum data as an input and training blood sugar profile data as atarget or with training first type spectrum data as an input and an AUCvalue of a training blood sugar profile calculated from the trainingblood sugar profile data as a target.

Also, the storage 920 may store programs or commands for operating theblood sugar estimation apparatus 900 and may store data which are inputor output.

The storage 920 may include a flash memory type memory, a hard disk typememory, a multimedia card micro type memory, a card type memory such asan SD memory, an XD memory, etc., an RAM, an SRAM, an ROM, an EEPROM, aPROM, a magnetic memory, a magnetic disk, an optical disk, etc. Also,the blood sugar estimation apparatus 900 may operate an external storagemedium such as a web storage which performs a storage function of thestorage 920 on the Internet.

The processor 930 may estimate blood sugar of the user based on themeasured second type spectrum data and the stored individualized bloodsugar estimation model.

FIG. 10 is a block diagram illustrating another example of a blood sugarestimation apparatus according to an exemplary embodiment.

Referring to FIG. 10 , compared with the blood sugar estimationapparatus 900, a blood sugar estimation apparatus 1000 may selectivelyfurther include an inputter 1010, a communicator 1020, and an outputter1030.

The inputter 1010 may receive various operation signals from a user.According to an exemplary embodiment, the inputter 1010 may include akey pad, a dome switch, a touch pad (static pressure/staticelectricity), a jog wheel, a jog switch, a hardware button, etc.Particularly, when a touch pad and a display together form a layerstructure, it may be called a touch screen.

The communicator 1020 may perform communication with an externalapparatus. For example, the communicator 1020 may transmit data inputthrough the inputter 1010, blood sugar of the user estimated by theprocessor 930, etc. to the external apparatus or may receive data forestimating the blood sugar of the user from the external apparatus.

Here, the external apparatus may be a medical apparatus which usesestimated blood sugar data of the user, a printer for outputtingresults, or a display apparatus which displays the estimated blood sugardata. In addition, the external apparatus may be a digital TV, a desktopcomputer, a cellular phone, a smart phone, a tablet PC, a notebook PC,PDA, a PMP, a navigation device, an MP3 player, a digital camera, awearable device, etc. but is not limited thereto.

The outputter 1030 may output data related to estimation of the bloodsugar of the user and estimation result data. According to an exemplaryembodiment, the outputter 1030 may output the data related to estimationof the blood sugar of the user and the estimation result data using atleast one of an acoustic method, a visual method, and a tactile method.For example, the outputter 1030 may output the data related toestimation of the blood sugar of the user and the estimation result datausing a voice, a text, vibrations, etc. For this, the outputter 1030 mayinclude a display, a speaker, a vibrator, etc. By way of an example,based on the estimation of the blood sugar level of the user, an alarmmay be output when the estimation of the blood sugar level of the userexceeds a predetermined threshold. In an exemplary embodiment, theapparatus which detects the blood sugar level 1000 may be a specializedmedical device which measures light reflected from the skin of the userto detect the user's sugar blood level, as described above by way of anexample.

FIG. 11 is a flowchart illustrating a method of estimating blood sugarbased on a heterogeneous spectrum according to an exemplary embodiment.

Referring to FIGS. 1 and 11 , the heterogeneous spectrum-based bloodsugar estimation apparatus 100 may generate a first type spectrum-bloodsugar profile relationship model through machine learning based ontraining first type spectrum data and training blood sugar profile data(in operation 1110). Here, a first type spectrum may be a Raman spectrumfor the skin and a blood sugar profile may be a trend of blood sugarlevel at predetermined times or a trend of blood sugar over time.

The heterogeneous spectrum-based blood sugar estimation apparatus 100may estimate blood sugar profile data of a user using the generatedfirst type spectrum-blood sugar profile relationship model (in operation1120). For example, the heterogeneous spectrum-based blood sugarestimation apparatus 100 may obtain first type spectrum data for theskin of the user and may estimate blood sugar profile data of the userby inputting the obtained first type spectrum data into the first typespectrum-blood sugar profile relationship model.

The heterogeneous spectrum-based blood sugar estimation apparatus 100may generate an individualized blood sugar estimation model throughmachine learning based on training second type spectrum data for theskin of the user and the estimated blood sugar profile data of the user(in operation 1130). Here, a second type spectrum may be an NIRspectrum.

For example, the heterogeneous spectrum-based blood sugar estimationapparatus 100 may generate the individualized blood sugar estimationmodel through machine learning with the training second type spectrumdata for the skin of the user as an input and a blood sugar valuecorresponding to the training second type spectrum data as a target.Here, the blood sugar value corresponding to the training second typespectrum data may be calculated from the estimated blood sugar profiledata of the user.

The heterogeneous spectrum-based blood sugar estimation apparatus 100may estimate blood sugar of the user using the individualized bloodsugar estimation model (in operation 1140). For example, theheterogeneous spectrum-based blood sugar estimation apparatus 100 mayobtain the second type spectrum data for the skin of the user and mayestimate the blood sugar of the user by inputting the obtained secondtype spectrum into the blood sugar estimation model.

FIG. 12 is a flowchart illustrating a method of generating a first typespectrum-based blood sugar profile relationship model according to anexemplary embodiment.

Referring to FIGS. 2 and 12 , according to an exemplary embodiment, thefirst type spectrum-blood sugar profile relationship model generationapparatus 200 may obtain training first type spectrum data and trainingblood sugar profile data (in operation 1210). Here, a first typespectrum may be a Raman spectrum.

For example, the first type spectrum-blood sugar profile relationshipmodel generation apparatus 200 may obtain training first type spectrumdata and the training blood sugar profile data from a certain databaseor an external apparatus. Here, the training blood sugar profile datamay be data obtained by performing an OGTT on a subject whose first typespectrum data has been measured.

The first type spectrum-blood sugar profile relationship modelgeneration apparatus 200 may generate a first type spectrum-blood sugarprofile relationship model through machine learning based on theobtained training first type spectrum data and training blood sugarprofile data (in operation 1220). For example, the first typespectrum-blood sugar profile relationship model generation apparatus 200may generate the first type spectrum-blood sugar profile relationshipmodel through machine learning with the training first type spectrumdata as an input and the training blood sugar profile data as a targetor with the training first type spectrum data as an input and an AUCvalue calculated from the training blood sugar profile data as a target.

FIG. 13 is a flowchart illustrating a method of estimating a blood sugarprofile according to an exemplary embodiment.

Referring to FIGS. 4 and 13 , the blood sugar profile estimationapparatus 400 may estimate first type spectrum data for the skin of auser (in operation 1310). Here, a first type spectrum may be a Ramanspectrum.

The blood sugar profile estimation apparatus 400 may estimate bloodsugar profile data of the user based on the measured first type spectrumdata and a stored first type spectrum-blood sugar profile relationshipmodel (in operation 1320). Here, the first type spectrum-blood sugarprofile relationship model may be generated through machine learningwith training first type spectrum data as an input and training bloodsugar profile data as a target or with training first type spectrum dataas an input and an AUC value of a training blood sugar profilecalculated from the training blood sugar profile data as a target.

According to an exemplary embodiment, when the first type spectrum-bloodsugar profile relationship model may be generated through machinelearning with the training first type spectrum data as the input and thetraining blood sugar profile data as the target, the blood sugar profileestimation apparatus 400 may estimate blood sugar profile data output byinputting the first type spectrum data into the first typespectrum-blood sugar profile relationship model as the blood sugarprofile data of the user.

According to another exemplary embodiment, when the first typespectrum-blood sugar profile relationship model is generated throughmachine learning with the training first type spectrum data as the inputand the AUC value of the blood sugar profile calculated from thetraining blood sugar profile data as the target, the blood sugar profileestimation apparatus 400 may estimate the blood sugar profile data ofthe user based on the AUC value output by inputting the measured firsttype spectrum data into the first type spectrum-blood sugar profilerelationship model.

FIG. 14 is a flowchart illustrating a method of generating a blood sugarestimation model according to an exemplary embodiment.

Referring to FIGS. 7 and 14 , the blood sugar estimation modelgeneration apparatus 700 may obtain blood sugar profile data of a user(in operation 1410). Here, the blood sugar profile data of the user isestimated based on a first type spectrum-blood sugar profilerelationship model and first type spectrum data for the skin of the userand may be obtained from a certain database or an external apparatus.Here, a first type spectrum may be a Raman spectrum.

Meanwhile, the first type spectrum-blood sugar profile relationshipmodel may be generated through machine learning with training first typespectrum data as an input and training blood sugar profile data as atarget or with training first type spectrum data as an input and an AUCvalue calculated from the training blood sugar profile data as a target.The training blood sugar profile data may be obtained by performing anOGTT on a subject whose training first type spectrum data has beenmeasured.

The blood sugar estimation model generation apparatus 700 may measuretraining second type spectrum data for the skin of the user (inoperation 1420). Here, a second type spectrum may be an NIR spectrum.

The blood sugar estimation model generation apparatus 700 may generatean individualized blood sugar estimation model based on the obtainedblood sugar profile data of the user and the measured training secondtype spectrum data (in operation 1430). For example, the blood sugarestimation model generation apparatus 700 may generate theindividualized blood sugar estimation model by calculating a blood sugarvalue corresponding to the training second type spectrum data from theblood sugar profile data of the user and through machine learning withthe training second type spectrum as an input and the calculated bloodsugar value as a target.

FIG. 15 is a flowchart illustrating a method of estimating blood sugaraccording to an exemplary embodiment.

Referring to FIGS. 9 and 15 , the blood sugar estimation apparatus 900may estimate second type spectrum data for the skin of a user (inoperation 1510). Here, a second type spectrum may be an NIR spectrum.

The blood sugar estimation apparatus 900 may estimate blood sugar of theuser based on the measured second type spectrum data and a storedindividualized blood sugar estimation model (in operation 1520). Here,the individualized blood sugar estimation model may be generated basedon blood sugar profile data of the user estimated by a first typespectrum-blood sugar profile relationship model and first type spectrumdata for the skin of the user and training second type spectrum data.For example, the individualized blood sugar estimation model may begenerated by calculating a blood sugar value corresponding to thetraining second type spectrum data from the estimated blood sugarprofile data of the user and through machine learning with the trainingsecond type spectrum as an input and the calculated blood sugar value asa target.

Meanwhile, the first type spectrum-blood sugar profile relationshipmodel may be generated through machine learning with training first typespectrum data as an input and training blood sugar profile data as atarget or with training first type spectrum data as an input and an AUCvalue of a training blood sugar profile calculated from the trainingblood sugar profile data as a target.

Exemplary embodiments can be implemented as computer readable codes in acomputer readable record medium. Codes and code segments constitutingthe computer program can be easily inferred by a skilled computerprogrammer in the art. The computer readable record medium includes alltypes of record media in which computer readable data are stored.Examples of the computer readable record medium include a ROM, a RAM, aCD-ROM, a magnetic tape, a floppy disk, and an optical data storage.Further, the record medium may be implemented in the form of a carrierwave such as Internet transmission. In addition, the computer readablerecord medium may be distributed to computer systems over a network, inwhich computer readable codes may be stored and executed in adistributed manner.

A number of examples have been described above. Nevertheless, it will beunderstood that various modifications may be made. For example, suitableresults may be achieved if the described techniques are performed indifferent order and/or if components in a described system,architecture, device, or circuit are combined in a different mannerand/or replaced or supplemented by other components or theirequivalents. Accordingly, other implementations are within the scope ofthe following claims.

What is claimed is:
 1. An apparatus comprising: a memory configured to store an individualized blood sugar estimation model; a second type spectrum measurement device configured to measure second type spectrum data for a skin of a user; and a processor configured to calculate a blood sugar value of the user based on the measured second type spectrum data and the individualized blood sugar estimation model, wherein the individualized blood sugar estimation model is generated based on blood sugar profile data of the user and previously measured second type spectrum data for the skin of the user, and wherein the blood sugar profile data of the user is calculated based on a first type spectrum-blood sugar profile relationship model.
 2. The apparatus of claim 1, wherein the first type spectrum-blood sugar profile relationship model is generated based on first type spectrum data, and wherein a first type spectrum is a Raman spectrum, and a second type spectrum is a near infrared (NIR) spectrum.
 3. The apparatus of claim 1, wherein the first type spectrum-blood sugar profile relationship model is generated through machine learning based on training first type spectrum data and training the blood sugar profile data.
 4. The apparatus of claim 3, wherein the training blood sugar profile data is obtained by performing an oral glucose tolerance test (OGTT) on a subject whose training first type spectrum data has been measured.
 5. The apparatus of claim 3, wherein the first type spectrum-blood sugar profile relationship model is generated through the machine learning with the training first type spectrum data as an input and the training blood sugar profile data as a target.
 6. The apparatus of claim 3, wherein the first type spectrum-blood sugar profile relationship model is generated through the machine learning with the training first type spectrum data as an input and an area under curve (AUC) value calculated from the training blood sugar profile data as a target.
 7. The apparatus of claim 3, wherein the machine learning comprises a machine learning algorithm selected from among partial least squares regression, linear regression, neural network, decision tree, genetic algorithm, genetic programming, K-nearest neighbor, radial basis function network, random, forest, support vector machine, and deep-learning.
 8. The apparatus of claim 1, wherein the second type spectrum measurer comprises: a light source configured to emit light to the skin of the user; and a spectroscope configured to detect absorbed, scattered, or reflected light from the skin of the user and to measure the second type spectrum data based on the detected light.
 9. The apparatus of claim 1, wherein the individualized blood sugar estimation model is generated through machine learning.
 10. An apparatus comprising: a memory configured to store an individualized blood sugar estimation model; a light source configured to emit light to a skin of a user; a spectroscope configured to detect light from the skin of the user and to measure second type spectrum data based on the detected light; and a processor configured to read the individualized blood sugar estimation model from the memory, and calculate a blood sugar value of the user based on the measured second type spectrum data and the individualized blood sugar estimation model, wherein the individualized blood sugar estimation model is generated based on blood sugar profile data of the user and previously measured second type spectrum data for the skin of the user, and wherein the blood sugar profile data of the user is calculated based on a first type spectrum-blood sugar profile relationship model.
 11. The apparatus of claim 10, wherein the first type spectrum-blood sugar profile relationship model is generated based on first type spectrum data, and wherein a first type spectrum is a Raman spectrum, and a second type spectrum is a near infrared (NIR) spectrum.
 12. The apparatus of claim 10, wherein the first type spectrum-blood sugar profile relationship model is generated through machine learning based on training first type spectrum data and training the blood sugar profile data.
 13. The apparatus of claim 12, wherein the training blood sugar profile data is obtained by performing an oral glucose tolerance test (OGTT) on a subject whose training first type spectrum data has been measured.
 14. The apparatus of claim 12, wherein the first type spectrum-blood sugar profile relationship model is generated through the machine learning with the training first type spectrum data as an input and the training blood sugar profile data as a target.
 15. The apparatus of claim 12, wherein the first type spectrum-blood sugar profile relationship model is generated through the machine learning with the training first type spectrum data as an input and an area under curve (AUC) value calculated from the training blood sugar profile data as a target.
 16. The apparatus of claim 12, wherein the machine learning comprises a machine learning algorithm selected from among partial least squares regression, linear regression, neural network, decision tree, genetic algorithm, genetic programming, K-nearest neighbor, radial basis function network, random, forest, support vector machine, and deep-learning.
 17. The apparatus of claim 10, wherein the individualized blood sugar estimation model is generated through machine learning. 